Scikit-learn: Machine Learning in Python

Article English OPEN
Pedregosa, Fabian; Varoquaux, Gaël; Gramfort, Alexandre; Michel, Vincent; Thirion, Bertrand; Grisel, Olivier; Blondel, Mathieu; Prettenhofer, Peter; Weiss, Ron; Dubourg, Vincent; Vanderplas, Jake; Passos, Alexandre; Cournapeau, David; Brucher, Matthieu; Perrot, Matthieu; Duchesnay, Édouard;
  • Publisher: Microtome Publishing
  • Subject: Python | [INFO.INFO-MS]Computer Science [cs]/Mathematical Software [cs.MS] | supervised learning | model selection | Python; supervised learning; unsupervised learning; model selection | [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG] | unsupervised learning

International audience; Scikit-learn is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems. This package focuses on bringing machine learning to non-specialists using a general-p... View more
  • References (16)
    16 references, page 1 of 2

    D. Albanese, G. Merler, S.and Jurman, and R. Visintainer. MLPy: high-performance Python package for predictive modeling. In NIPS, MLOSS workshop, 2008.

    C.C. Chang and C.J. Lin. LIBSVM: a library for support vector machines. http://www., 2001.

    P.F. Dubois, editor. Python: batteries included, volume 9 of Computing in Science & Engineering. IEEE/AIP, May 2007.

    R.E. Fan, K.W. Chang, C.J. Hsieh, X.R. Wang, and C.J. Lin. LIBLINEAR: A library for large linear classification. The Journal of Machine Learning Research, 9:1871-1874, 2008.

    J. Friedman, T. Hastie, and R. Tibshirani. Regularization paths for generalized linear models via coordinate descent. Journal of statistical software, 33(1):1, 2010.

    I Guyon, S. R. Gunn, A. Ben-Hur, and G. Dror. Result analysis of the NIPS 2003 feature selection challenge, 2004.

    M. Hanke, Y.O. Halchenko, P.B. Sederberg, S.J. Hanson, J.V. Haxby, and S. Pollmann. PyMVPA: A Python toolbox for multivariate pattern analysis of fMRI data. Neuroinformatics, 7(1):37-53, 2009.

    T. Hastie and B. Efron. Least Angle Regression, Lasso and Forward Stagewise. http: //, 2004.

    V. Michel, A. Gramfort, G. Varoquaux, E. Eger, C. Keribin, and B. Thirion. A supervised clustering approach for fMRI-based inference of brain states. Patt Rec, page epub ahead of print, April 2011. doi: 10.1016/j.patcog.2011.04.006.

    K.J. Milmann and M. Avaizis, editors. Scientific Python, volume 11 of Computing in Science & Engineering. IEEE/AIP, March 2011.

  • Metrics
Share - Bookmark